2002. Battery Storage Planning Under Uncertainty
Invited abstract in session WC-44: Improving data and methods for energy system investment, stream Energy Economics & Management.
Wednesday, 12:30-14:00Room: Newlyn 1.01
Authors (first author is the speaker)
| 1. | Mohammad Fattahi
|
| Marketing, Operations, and Systems, Northumbria University, Newcastle Business School |
Abstract
The transition to renewable energy is increasing the share of renewable energy sources (RES) in power grids. However, their variability and intermittency create challenges such as load shedding and system congestion. This study examines how battery storage systems (BSS) can address these issues by balancing electricity supply and demand. A two-stage stochastic optimization model is used to determine the optimal location, size, and type of batteries, with the second stage focusing on hourly operational decisions over a year. Unlike previous studies, this research incorporates both the technical and economic characteristics of battery technologies. The New York State (NYS) power system, currently transitioning to higher renewable energy integration, serves as the case study. Using historical load and weather data from 1980 to 2019, the study accounts for uncertainties in load and RES generation through a sample average approximation approach. Results indicate that BSS can reduce renewable curtailment by 34% and load shedding by 21%, enhancing grid resilience and supporting NYS’s 2030 energy targets. Additionally, the cost of expanding BSS capacity does not increase linearly, highlighting a complex relationship between costs and renewable penetration. This research provides valuable insights for the strategic deployment of BSS to create a cost-effective and reliable power grid while assessing the feasibility of NYS’s 2030 energy goals.
Keywords
- Energy Policy and Planning
- OR in Energy
- Programming, Stochastic
Status: accepted
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